PROJECT TITLE :

High-Resolution Inverse Synthetic Aperture Radar Imaging and Scaling With Sparse Aperture

ABSTRACT:

In high-resolution radar imaging, the rotational motion of targets generally produces migration through resolution cells (MTRC) in inverse artificial aperture radar (ISAR) pictures. Sometimes, it is a challenge to appreciate correct MTRC correction on sparse aperture (SA) knowledge, which tends to degrade the performance of translational motion compensation and SA-imaging. During this paper, we gift a unique algorithm for top-resolution ISAR imaging and scaling from SA knowledge, which effectively incorporates the translational motion phase error and MTRC corrections. During this algorithm, the ISAR image formation is converted into a sparsity-driven optimization via most a posterior (MAP) estimation, where the statistics of an ISAR image is modeled as advanced Laplace distribution to produce a sparse previous. The translational motion phase error compensation and cross-vary MTRC correction are modeled as joint vary-invariant and vary-variant phase error corrections in the vary-compressed part history domain. Our proposed imaging approach is performed by a 2-step method: 1) the range-invariant and vary-variant section error estimations using a metric of minimum entropy are utilized and solved by employing a coordinate descent technique to understand a rough phase error correction. Meanwhile, the rotational motion will be obtained from the estimation of vary-variant part errors, that is employed for ISAR scaling in the cross-vary dimension; a pair of) below a two-dimensional (two-D) Fourier-based mostly dictionary by involving the slant-range MTRC, joint MTRC-corrected ISAR imaging and accurate part adjustment are realized by solving the sparsity-driven optimization with SA knowledge, where the residual part errors are treated as model error and removed to realize a fine correction. Finally, some experiments primarily based on simulated and measured information are performed to verify the effectiveness of the proposed algorithm.


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